Google I/O 2024: Construct a Cat Chatbot utilizing Gemini on Android

Gemini is a household of synthetic intelligence (AI) fashions launched by Google, with every mannequin specializing in particular use circumstances. At I/O 2024, Google introduced the Gemini 1.5 Professional and Gemini 1.5 Flash fashions. These fashions can be found through the Google AI Consumer SDK.

On this tutorial, you’ll create an AI chatbot named CatBot utilizing the Gemini 1.5 Professional mannequin. On this chatbot, you’ll work together with a enjoyable cat named Milo.

In the course of the course of, you’ll study to:

• Setup the Google AI API Key.
• Configure and combine the Gemini mannequin.
• Create a chat UI.
• Add security checks.

And with that, it’s time to get began.

Getting Began

Obtain the supplies for this tutorial by clicking the Obtain Supplies button on the prime or backside of the tutorial. Then, open the starter venture in Android Studio Jellyfish or later.

You’ll work on CatBot, an AI-based chatbot that allows you to chat with a cat named Milo.

The venture comprises the next recordsdata:

• MainActivity: Comprises the principle Exercise and hosts the Composables.
• ChatMessage: An information class representing every message.
• ChatScreen: A Composable describing the chat display.
• ChatViewModel: A ViewModel representing the state of the chat display. It’ll include the logic of dealing with outgoing and incoming messages.

Construct and run the app. You’ll see the next display:

The display has an enter subject for the chat message and a ship button. Proper now, sending a message doesn’t do something. You’ll change this all through the tutorial.

Producing the API key

First, you’ll want an API key to work together with the Gemini APIs. Head over to https://aistudio.google.com/app which can open the Google AI Studio. On the proper aspect of the studio, you’ll see the Mannequin dropdown:

Choose the Gemini 1.5 Flash mannequin.

Though the Gemini 1.5 Professional mannequin is extra highly effective, the Gemini 1.5 Flash is considerably sooner, making it extra appropriate for this chatbot software.

Subsequent, click on Get API key on the left navigation panel:

You’ll get the next display should you haven’t created an API key earlier:

Click on Create API key. You’ll get the Create API Key dialog as proven beneath:

Choose Create API key in new venture. As soon as the API key has been generated, you’ll see a dialog together with your new API key. Copy the API Key and head again to Android Studio.

Open native.properties and add the next code:

apiKey=your API key right here

Within the code above, substitute your API key right here with the API key you copied earlier.

Observe: This methodology of specifying the API key contained in the Android venture is barely appropriate for prototypes. For manufacturing apps, the API key needs to be current on the backend, and entry to the mannequin ought to solely be accomplished through an API.

Now that the API secret’s prepared, you can begin modeling the chat message.

Modeling the Chat Message

On this chatbot, there will be three varieties of messages:

1. Consumer messages
2. Replies from the mannequin
3. Error messages

To mannequin the varieties of messages, create a brand new class named ChatParticipant and add the next code:

enum class ChatParticipant {
  USER,
  AI,
  ERROR
}

Within the code above, you created an enum class with three attainable values, every representing a kind of message.

Subsequent, it’s essential affiliate every chat message with a specific participant. Open ChatMessage and add the next attribute to the information class:

val participant: ChatParticipant

The ChatMessage class will now be as follows:

knowledge class ChatMessage(
  val id: String = UUID.randomUUID().toString(),
  val message: String,
  val participant: ChatParticipant
)

Configuring the Gemini Mannequin

You’ll want the Google AI Consumer SDK to entry the Gemini mannequin on Android. Open the app-module construct.gradle and add the next dependency:

implementation("com.google.ai.consumer.generativeai:generativeai:0.6.0")

Do a Gradle sync and look ahead to the dependency to complete downloading.

Subsequent, create a brand new file named Mannequin.kt and add the next code:

inner val mannequin = GenerativeModel(
  // 1
  modelName = "gemini-1.5-flash-latest",
  // 2
  apiKey = BuildConfig.apiKey,
  // 3
  generationConfig = generationConfig {
    temperature = 0.7f
  },
  // 4
  systemInstruction = content material {
    textual content("You're a enjoyable cat named Milo. " +
        "Give mischievous solutions in most 3 strains. " +
        "Attempt to hold the dialog going")
  }
)

The code above creates a brand new occasion of GenerativeModel with the next arguments:

• modelName: Because you’re utilizing Gemini 1.5 Flash, the modelName is gemini-1.5-flash-latest. Within the case of Gemini 1.5 Professional, the mannequin title can be gemini-1.5-pro-latest.
• apiKey: This worth is extracted from the native.properties worth you set earlier within the tutorial.
• generationConfig: The mannequin configuration. Right here, you set the temperature worth to 0.7. The temperature will be something between 0 and 1. A decrease temperature will result in a extra predictable response, whereas the next temperature will result in a extra artistic response.
• systemInstruction: That is the bottom immediate in your mannequin, which can decide the persona of your mannequin. For this app, you’re asking the mannequin to behave like a enjoyable cat named Milo and offering extra particulars.

Observe: Don’t import the BuildConfig class from the Google AI Consumer SDK. While you construct the venture, the wanted BuildConfig might be generated.

Including Preliminary Historical past

When engaged on a dialog app utilizing the Gemini API, you may add message historical past together with the system immediate. This allows you to present the mannequin with the context of a earlier dialog so the person can proceed a dialog throughout app periods.

Open ChatViewModel and alter the constructor to:

class ChatViewModel(
  generativeModel: GenerativeModel = mannequin
)

ChatViewModel now takes an occasion of GenerativeModel as a constructor argument, and the default worth is ready to the occasion you created within the earlier part.

Subsequent, you’ll want to supply the chat historical past. Add the next code contained in the ChatViewModel class:

// 1
non-public val chat = generativeModel.startChat(
  // 2
  historical past = listOf(
    //3
    content material("person") {
      textual content("Hey n")
    },
    content material("mannequin") {
      textual content("Meow!  What's up, human?  Did you carry me any tuna?  😉 n")
    }
  )
)

Within the code above, you:

1. Begin a brand new chat with the startChat methodology.
2. Specify the historical past argument as a listing of messages.
3. Specify that the person despatched the primary message and the mannequin despatched the second.

Now that the mannequin has the context of the message historical past, the UI ought to show the messages if you open the app.

Change the initialization of _uiState:

non-public val _uiState: MutableStateFlow<Checklist<ChatMessage>> =
  MutableStateFlow(
    chat.historical past.map { content material ->
      ChatMessage(
        message = content material.components.first().asTextOrNull() ?: "",
          participant = if (content material.position == "person")
            ChatParticipant.USER
          else
            ChatParticipant.AI,
      )
    }
  )

Within the code above, you iterate over the chat historical past and map every message to an occasion of ChatMessage. You then set the default worth of the state to include the message historical past.

Now, each time you run the app, a dialog historical past might be obtainable, making it straightforward to proceed the dialog.